Example Simulations

ASSUME provides a range of example simulations to help users understand and explore various market scenarios. These examples demonstrate different features and configurations, from small-scale setups to large, real-world simulations. Below is an overview of the available examples, followed by a more detailed explanation of their key features.

Overview of Example Simulations

Example Name	Input Files	Description
small	example_01a	Basic simulation with 4 actors and single-hour bidding.
small_dam	example_01a	Day-ahead market simulation with 24-hour bidding.
small_with_opt_clearing	example_01a	Demonstrates optimization-based market clearing.
small_with_vre	example_01b	Introduces variable renewable energy sources.
small_with_vre_and_storage	example_01c	Showcases renewable energy and storage units.
small_with_BB_and_LB	example_01c	Illustrates block bids and linked bids usage.
small_with_vre_and_storage_and_complex_clearing	example_01c	Combines VRE, storage, and complex clearing mechanisms.
small_with_crm	example_01c	Includes Control Reserve Market (CRM).
small_with_redispatch	example_01d	Demonstrates redispatch scenarios.
small_with_nodal_clearing	example_01d	Features nodal market clearing.
small_with_zonal_clearing	example_01d	Implements zonal market clearing.
market_study_eom	example_01f	Showcases comparison of single market to multi market. Case 1 in [3]
market_study_eom_and_ltm	example_01f	Showcases simulation with EOM and LTM market. Case 2 in [3]
small_learning_1	example_02a	7 power plants, 1 with learning bidding strategy. Case 1 in [1]
small_learning_2	example_02b	11 power plants, 5 with learning bidding strategy. Case 2 in [1]
small_learning_3	example_02c	16 power plants, 10 with learning bidding strategy. Case 3 in [1]
learning_with_complex_bids	example_02d	Learning strategies with complex bidding.
large_2019_eom	example_03	Full-year German power market simulation (EOM only). [2]
large_2019_eom_crm	example_03	Full-year German power market simulation (EOM + CRM). [2]
large_2019_day_ahead	example_03	Full-year German day-ahead market simulation. [2]
large_2019_with_DSM	example_03	Full-year German market simulation with Demand Side Management. [2]
large_2019_rl	example_03a	Full-year 2021 German market simulation with reinforcement learning with modified power plants list. [1]
large_2021_rl	example_03b	Full-year 2021 German market simulation with reinforcement learning with modified power plants list. [1]

Detailed Features of Example Simulations

The following table provides a more in-depth look at key examples, highlighting their specific characteristics and configurations.

Example Name	Country	Generation Tech	Generation Volume	Demand Tech	Demand Volume	Markets	Bidding Strategy	Grid	Further Info
small_learning_1	Germany	Conventional	12,500 MW	Fixed inflexible	1,000,000 MW	EOM	Learning, Naive	No	Case 1 from [1]
small_learning_2	Germany	Conventional	12,500 MW	Fixed inflexible	1,000,000 MW	EOM	Learning, Naive	No	Case 2 from [1]
small_learning_3	Germany	Conventional	12,500 MW	Fixed inflexible	1,000,000 MW	EOM	Learning, Naive	No	Case 3 from [1]
large_2019_eom	Germany	Conv., VRE	Full 2019 data	Fixed inflexible	Full 2019 data	EOM	Various	No	Based on [2]
large_2019_eom_crm	Germany	Conv., VRE	Full 2019 data	Fixed inflexible	Full 2019 data	EOM, CRM	Various	No	Based on [2]
large_2019_day_ahead	Germany	Conv., VRE	Full 2019 data	Fixed inflexible	Full 2019 data	DAM	Various	No	Based on [2]
large_2019_with_DSM	Germany	Conv., VRE	Full 2019 data	Fixed, Flexible (DSM)	Full 2019 data	EOM	Various	No	Based on [2]
large_2019_rl	Germany	Conv., VRE	Full 2019 data	Fixed inflexible	Full 2019 data	EOM	RL, Various	No	Based on [1]
large_2021_rl	Germany	Conv., VRE	Full 2021 data	Fixed inflexible	Full 2021 data	EOM	RL, Various	No	Based on [1]

Note

Conv. = Conventional, VRE = Variable Renewable Energy, EOM = Energy-Only Market, CRM = Control Reserve Market, DAM = Day-Ahead Market, RL = Reinforcement Learning, DSM = Demand Side Management

Key Features of Example Simulations

1. Small-scale examples (small_*):

   • Designed for easier understanding of specific features and configurations.

   • Demonstrate various market mechanisms, bidding strategies, and technologies.

   • Useful for learning ASSUME’s basic functionalities and exploring specific market aspects.

2. Learning-enabled examples (small_learning_*, learning_with_complex_bids):

   • Showcase the integration of learning algorithms in bidding strategies.

   • Illustrate how agents can adapt their behavior in different market conditions.

   • small_learning_1, small_learning_2, and small_learning_3 directly correspond to Cases 1, 2, and 3, respectively, in the publication by Harder et al. [1].

   • Demonstrate practical applications of reinforcement learning in energy markets.

3. Large-scale examples (large_2019_*, large_2021_rl):

   • Represent real-world scenarios based on the German power market in 2019 and 2021.

   • Include full demand and renewable generation profiles, major generation units, and storage facilities.

   • Demonstrate different market configurations (EOM, CRM, DAM) and their impacts.

   • The large_2019_with_DSM example incorporates steel plants as flexible demand side units, showcasing Demand Side Management capabilities.

   • large_2019_rl and large_2021_rl examples apply reinforcement learning techniques to full-year market simulations, as presented in [1]. In this examples, the power plant units with a capacity of less then 300 MW were aggregated into larger units to increase the learning speed.

   • Based on comprehensive research presented in [1] and [2], offering insights into complex market dynamics and the application of advanced learning techniques in different market years.

These examples provide a diverse range of scenarios, allowing users to explore various aspects of energy market simulation, from basic concepts to complex, real-world applications and advanced learning strategies.

References

[1] (1,2,3,4,5,6,7,8,9,10,11,12,13)

Harder, N.; Qussous, R.; Weidlich, A. Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. Energy and AI 2023. 14. 100295. https://doi.org/10.1016/j.egyai.2023.100295.

[2] (1,2,3,4,5,6,7,8,9)

Qussous, R.; Harder, N.; Weidlich, A. Understanding Power Market Dynamics by Reflecting Market Interrelations and Flexibility-Oriented Bidding Strategies. Energies 2022, 15, 494. https://doi.org/10.3390/en15020494

[3] (1,2)

Maurer, F.; Miskiw, K.; Ramirez, R.; Harder, N.; Sander, V.; Lehnhoff, S. Abstraction of Energy Markets and Policies - Application in an Agent-Based Modeling Toolbox. Energy Informatics 2023, http://doi.org/10.1007/978-3-031-48652-4_10